World's Best Scientists 2026 revealed!
George Em Karniadakis

George Em Karniadakis

Award Badge
Mathematics
USA
2026
Award Badge
Mechanical and Aerospace Engineering
USA
2026

D-Index & Metrics

Mathematics

D-Index
139
Citations
103227
World Ranking
1
National Ranking
1

Mechanical and Aerospace Engineering

D-Index
133
Citations
99712
World Ranking
9
National Ranking
5

Physics

D-Index
135
Citations
99796
World Ranking
462
National Ranking
258

Research.com Recognitions

  • 2026 - Research.com Mathematics in United States Leader Award
  • 2026 - Research.com Mechanical and Aerospace Engineering in United States Leader Award
  • 2025 - Research.com Mathematics in United States Leader Award
  • 2025 - Research.com Mechanical and Aerospace Engineering in United States Leader Award
  • 2018 - Fellow of the American Association for the Advancement of Science (AAAS)
  • 2013 - THE J. TINSLEY ODEN MEDAL For outstanding contributions to stochastic differential equations, in particular modelling uncertainty with polynomial chaos and development of spectral and hp element methods on unstructured meshes
  • 2011 - ACM Gordon Bell Prize For "A new computational paradigm in multiscale simulations: Application to brain-blood flow."
  • 2010 - SIAM Fellow For contributions to stochastic modeling, spectral elements, and fluid mechanics.
  • 2007 - THE THOMAS J.R. HUGHES MEDAL
  • 2004 - Fellow of American Physical Society (APS) Citation For his innovative developments and his insightful applications of the spectralelement method in computational fluid dynamics
  • 2002 - Fellow of the American Society of Mechanical Engineers

Overview

George Em Karniadakis is affiliated with Brown University in the United States. Their research work spans multiple fields of study, with a significant focus on engineering and physics and astronomy.

Their main subfields of study include statistical and nonlinear physics, artificial intelligence, computational mechanics, aerospace engineering, and materials chemistry. Their research topics cover model reduction and neural networks, neural networks and applications, fluid dynamics and turbulent flows, probabilistic and robust engineering design, nuclear engineering thermal-hydraulics, blood properties and coagulation, and Gaussian processes and Bayesian inference.

George Em Karniadakis has contributed to several research publications and academic venues. Frequent publication venues include:

  • arXiv (Cornell University)
  • Journal of Computational Physics
  • Computer Methods in Applied Mechanics and Engineering
  • SSRN Electronic Journal
  • SIAM Journal on Scientific Computing

Recent notable papers authored or co-authored by George Em Karniadakis are:

  • Physics-informed machine learning, 2021, Nature Reviews Physics
  • Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations, 2020, Science
  • Physics-informed neural networks (PINNs) for fluid mechanics: a review, 2021, Acta Mechanica Sinica
  • NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations, 2020, Journal of Computational Physics
  • Physics-Informed Neural Networks for Heat Transfer Problems, 2021, Journal of Heat Transfer

The scientist has co-authored extensively with collaborators including Khemraj Shukla, Somdatta Goswami, Zongren Zou, Xuhui Meng, and Ameya D. Jagtap.

In addition to journal publications, George Em Karniadakis has contributed to academic books, notably one published by Cambridge University Press titled Spectral and Spectral Element Methods for Fractional Ordinary and Partial Differential Equations in 2024.

Awards received by George Em Karniadakis include:

  • Fellow of the American Association for the Advancement of Science (AAAS), 2018
  • THE J. TINSLEY ODEN MEDAL, 2013, for contributions to stochastic differential equations and development of spectral and hp element methods on unstructured meshes
  • ACM Gordon Bell Prize, 2011, for work on multiscale brain-blood flow simulations
  • SIAM Fellow, 2010, for contributions to stochastic modeling, spectral elements, and fluid mechanics
  • THE THOMAS J.R. HUGHES MEDAL, 2007
  • Fellow of American Physical Society (APS), 2004, for developments in computational fluid dynamics via spectral-element methods
  • Fellow of the American Society of Mechanical Engineers, 2002

Best Publications

  • Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

    Maziar Raissi;Paris Perdikaris;George E. Karniadakis

  • The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations

    Dongbin Xiu;George Em Karniadakis

  • Physics-informed machine learning

    George Em Karniadakis;Ioannis G. Kevrekidis;Lu Lu;Paris Perdikaris

  • Spectral/hp Element Methods for Computational Fluid Dynamics

    George Karniadakis;Spencer J. Sherwin

  • Microflows and Nanoflows: Fundamentals and Simulation

    George E Karniadakis

  • DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators

    Lu Lu;Pengzhan Jin;George Em Karniadakis

  • The Development of Discontinuous Galerkin Methods

    Bernardo Cockburn;George E. Karniadakis;Chi-Wang Shu

  • Physics-informed neural networks (PINNs) for fluid mechanics: a review

    Unknown

  • High-order splitting methods for the incompressible Navier-Stokes equations

    George Em Karniadakis;Moshe Israeli;Steven A Orszag

  • Modeling uncertainty in flow simulations via generalized polynomial chaos

    Dongbin Xiu;George Em Karniadakis

  • Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations.

    Maziar Raissi;Maziar Raissi;Alireza Yazdani;George Em Karniadakis

  • DeepXDE: A deep learning library for solving differential equations

    Lu Lu;Xuhui Meng;Zhiping Mao;George Em Karniadakis

  • REPORT: A MODEL FOR FLOWS IN CHANNELS, PIPES, AND DUCTS AT MICRO AND NANO SCALES

    Ali Beskok;George Em Karniadakis

  • Spectral/hp Element Methods for CFD

    George Em Karniadakis;Spencer J Sherwin

  • Hidden physics models: Machine learning of nonlinear partial differential equations

    Maziar Raissi;George Em Karniadakis

  • Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations

    Maziar Raissi;Paris Perdikaris;George Em Karniadakis

  • Physics-informed neural networks for high-speed flows

    Zhiping Mao;Ameya D. Jagtap;George Em Karniadakis

  • NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations

    Xiaowei Jin;Shengze Cai;Hui Li;George Em Karniadakis

  • Discontinuous Galerkin Methods: Theory, Computation and Applications

    Bernardo Cockburn;George E. Karniadakis;Chi-Wang Shu

  • Physics-informed neural networks for inverse problems in nano-optics and metamaterials.

    Yuyao Chen;Lu Lu;George Em Karniadakis;Luca Dal Negro

  • Modeling Uncertainty in Steady State Diffusion Problems via Generalized Polynomial Chaos

    Dongbin Xiu;George Em Karniadakis

  • An adaptive multi-element generalized polynomial chaos method for stochastic differential equations

    Xiaoliang Wan;George Em Karniadakis

  • Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators

    Lu Lu;Pengzhan Jin;Pengzhan Jin;Guofei Pang;Zhongqiang Zhang

Frequent Co-Authors

Paris Perdikaris
Paris Perdikaris University of Pennsylvania
Spencer J. Sherwin
Spencer J. Sherwin Imperial College London
Suchuan Dong
Suchuan Dong Purdue University West Lafayette
Martin R. Maxey
Martin R. Maxey Brown University
Robert M. Kirby
Robert M. Kirby University of Utah
Dongbin Xiu
Dongbin Xiu The Ohio State University
Guang Lin
Guang Lin Purdue University West Lafayette

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